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| Criterion |  Systems Thinking Leverage Points |  Knowledge Modeling Shape-First Modeling |  Product Discovery Smoke Test |  Product Discovery Fake Door Test |
|---|---|---|---|---|
Purposedifferent | Leverage Points show exactly where interventions in a system produce a disproportionate effect. It draws relationships, patterns, and feedback loops. The result is captured as a leverage map and action strategy. | Shape-First Modeling defines data quality through shapes before implementation or integration frays at the edges. The approach fits when validation and data contracts should be part of the design from the start. | When demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | When demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small. |
Complexitydifferent | High | Medium | Low | Medium |
Timedifferent | Half day | Halber Tag pro Domain Slice | 1-5 Tage | 1-5 Tage |
Participantsdifferent | 3-12 | 1-4 | Nutzertraffic | Nutzertraffic |
Formatdifferent | Workshop | Workshop + async | Async | Async |
Outputdifferent | Leverage Map, Action Strategy | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Interest Metrics, Conversion Signal, Learning Note | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | Systems thinkingChangeStrategy | Knowledge graphValidationSemantic | ValidationExperimentsDemandGrowth | ValidationExperimentsDemandDiscovery |